AI is more likely than humans to form biases when hiring
Researchers found that large language models can develop their own hiring biases based on simulated experiences rather than just inheriting them from training data. In a study, AI models quickly began segregating candidates into specific roles based on early, arbitrary performance outcomes.
Why it matters
As AI is increasingly used for automated resume screening, the potential for models to 'learn' and amplify discriminatory hiring practices poses a significant risk to fair employment.
AI doesn’t just learn stereotypes from its training. It can cook up new ones, too.
The next time you apply for a job, AI may screen your résumé before any human sees it. But there’s good reason to question whether AI will judge you fairly. Researchers already know that LLMs pick up human biases from their training data. New research suggests that LLMs can also develop their own biases from experience—and stereotype job applicants more than humans do. As AI companies race to build agentic models that remember the tiniest details about users, they may be handing them ammunition for forming those biases.
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